Version Compatibility
Reference examples tested with: ChromHMM 1.27+, Segway 3.0+, EpiSegMix 1.0+, EpiLogos (Meuleman lab), IDEAS 1.20+, samtools 1.19+, bedtools 2.31+. ChromHMM requires Java 8+; runs as java -mx<MEMORY> -jar ChromHMM.jar <command>.
Chromatin State Segmentation
"Integrate multiple histone modification ChIP-seq tracks into chromatin states" -> Learn a small set of recurring combinatorial patterns of histone marks (active promoter, active enhancer, poised enhancer, polycomb-repressed, heterochromatic, transcribed, etc.) and segment the genome by which state each region belongs to. Output: per-state genomic intervals, state-by-mark emission matrix, and state-state transition matrix.
- CLI (canonical): ChromHMM
BinarizeBam->LearnModel->OverlapEnrichment/NeighborhoodEnrichment - CLI (continuous signal): Segway
train->posterior->annotate - CLI (flexible distributions): EpiSegMix (2024)
- Visualization across biosamples: EpiLogos (Meuleman lab)
- Cell-type-aware joint: IDEAS
Chromatin state segmentation requires a panel of histone marks; minimum 4-5 marks (e.g., H3K4me3, H3K27ac, H3K4me1, H3K36me3, H3K27me3) for meaningful states. With fewer marks, simpler peak-based annotation (chipseq/peak-annotation) is more appropriate.
Tool Taxonomy
| Tool | Method | Strength | Fails when |
|---|---|---|---|
| ChromHMM (Ernst & Kellis 2012; v1.27 current) | Multivariate HMM on binarized 200 bp bins | Canonical; widely used; integrated with Roadmap Epigenomics 15-state model; mature toolchain | Binarization throws away signal quantitation; default 200 bp bins may be too coarse for sharp boundaries |
| Segway (Hoffman 2012) | Dynamic Bayesian Network on continuous signal | Higher resolution; uses signal magnitudes not binarized | More complex setup; slower; less standardized output |
| EpiSegMix (Schmitz, Aggarwal, Laufer, Walter, Salhab, Rahmann 2024 Bioinformatics 40:btae178) | HMM with flexible read-count distributions + duration modeling | Modern; handles both narrow and broad mark distributions in one model | Newer; smaller user base |
| EpiLogos (Meuleman lab) | Multi-biosample visualization tool | Built on top of ChromHMM/Segway segmentations; compare ChromHMM states across 100s of biosamples | Visualization tool, not a segmentation method itself |
| IDEAS (Zhang 2016) | Cell-type-aware joint inference | Across-cell-type segmentation respecting cell-type identity | Slower; complex parameter tuning |
| EpiCSeg (Mammana 2015) | Negative binomial mixture | Read-count-based; doesn't need binarization | Less standardized output |
| GenoSTAN | HMM with various emission distributions | Flexible | Less actively developed |
| Roadmap 25-state model (Kundaje 2015) | ChromHMM 25-state precomputed model | Reference for cross-cell-type interpretation | Requires the Roadmap imputed 12-mark panel |
| Full-stack ChromHMM (Vu Ernst 2022) | 100-state segmentation across 1032 datasets / 127 reference epigenomes | Comprehensive cross-tissue annotation | Computationally intensive to retrain |
ChromHMM Workflow
ChromHMM is the de facto standard. The workflow has 4 stages:
Step 1: Binarize ChIP-seq signal
bash# Build cellMarkFileTable: cell_type<TAB>mark<TAB>file<TAB>(optional control) cat > cellMarkFileTable.txt << EOF GM12878 H3K4me3 gm12878_h3k4me3.bam gm12878_input.bam GM12878 H3K27me3 gm12878_h3k27me3.bam gm12878_input.bam GM12878 H3K27ac gm12878_h3k27ac.bam gm12878_input.bam GM12878 H3K4me1 gm12878_h3k4me1.bam gm12878_input.bam GM12878 H3K36me3 gm12878_h3k36me3.bam gm12878_input.bam EOF # Binarize BAMs into 200 bp bins; emission = whether mark exceeds Poisson threshold java -mx16G -jar ChromHMM.jar BinarizeBam \ -b 200 \ chromsizes_hg38.txt \ bam_dir/ \ cellMarkFileTable.txt \ binarized_output/
Output: per-chromosome _binary.txt files, one row per 200 bp bin, columns = marks, values 0/1.
Step 2: Learn model
bash# Train HMM with N states; common choices: 15, 18, 25 # 15 states: Ernst & Kellis 2011 model; canonical # 18 states: extends with additional regulatory states # 25 states: Roadmap Epigenomics extended model java -mx16G -jar ChromHMM.jar LearnModel \ -p 8 \ binarized_output/ \ model_15state/ \ 15 \ hg38 # Output: model_15state.txt (emission + transition matrices), # emissions_15.png (visualization), transitions_15.png, # per-chromosome _segments.bed (state assignments) # AND automatically runs OverlapEnrichment + NeighborhoodEnrichment
Step 3: Interpret states from emission matrix
| Roadmap 15-state assignments (canonical) |
|---|
| 1_TssA — Active TSS (high H3K4me3, H3K27ac) |
| 2_TssAFlnk — Flanking TSS (H3K4me3, H3K27ac, lower) |
| 3_TxFlnk — Transcript flanking |
| 4_Tx — Strong transcription (H3K36me3, H3K79me2 if available) |
| 5_TxWk — Weak transcription |
| 6_EnhG — Enhancer in gene body (H3K4me1, H3K27ac) |
| 7_Enh — Generic enhancer (H3K4me1, H3K27ac) |
| 8_ZNF/Rpts — Zinc-finger / repeats |
| 9_Het — Heterochromatin (H3K9me3) |
| 10_TssBiv — Bivalent TSS (H3K4me3 + H3K27me3) |
| 11_BivFlnk — Bivalent flanking |
| 12_EnhBiv — Bivalent enhancer (H3K4me1 + H3K27me3) |
| 13_ReprPC — Polycomb-repressed (H3K27me3) |
| 14_ReprPCWk — Weak Polycomb |
| 15_Quies — Quiescent (no signal) |
Step 4: Functional enrichment of states
bash# OverlapEnrichment: enrichment of each state for external feature sets. # Options (e.g. -labels) MUST precede the three positional args. java -mx16G -jar ChromHMM.jar OverlapEnrichment \ -labels \ model_15state/GM12878_15_segments.bed \ /path/to/anchor_files/ \ enrichment_output/GM12878 # NeighborhoodEnrichment: enrichment relative to anchor positions (e.g., TSS) java -mx16G -jar ChromHMM.jar NeighborhoodEnrichment \ -labels \ model_15state/GM12878_15_segments.bed \ /path/to/tss_anchors.txt \ enrichment_output/GM12878_TSS
Anchor files: BED files of features (CGIs, repeats, conserved elements, etc.) for OverlapEnrichment; position files for NeighborhoodEnrichment.
Choosing State Count
| States | Use case | Mark panel size |
|---|---|---|
| 8-10 | Initial exploration; small mark panel (3-4 marks) | 3-5 marks |
| 15 | Roadmap Epigenomics canonical | 5 core (H3K4me3, H3K4me1, H3K36me3, H3K27me3, H3K9me3) |
| 18 | Roadmap extended (adds H3K27ac -> fine enhancer subtypes) | 6 marks (core 5 + H3K27ac) |
| 25 | Roadmap Epigenomics extended; cross-cell-type compatibility | 12 imputed marks |
| 50+ | Full-stack model (Vu Ernst 2022) | Many marks across many cell types |
Practical workflow: Train at N=15, 18, 25; compare emission matrices; choose the smallest N where biology is interpretable. Higher N risks over-segmentation (state splitting random variation).
Segway Workflow
bash# Segway reads only the Genomedata format, so first pack the signal tracks # (one track per bigWig) plus the genome sequence into an archive. genomedata-load \ -s hg38.fa \ -t h3k4me3=h3k4me3.bw \ -t h3k27ac=h3k27ac.bw \ -t h3k4me1=h3k4me1.bw \ -t h3k36me3=h3k36me3.bw \ -t h3k27me3=h3k27me3.bw \ signal.genomedata # Train Segway model; GENOMEDATA and TRAINDIR are positional segway train \ --num-labels=25 \ --num-instances=3 \ --resolution=100 \ signal.genomedata traindir/ # Posterior probabilities + hard-call annotation (GENOMEDATA TRAINDIR OUTDIR, positional) segway posterior signal.genomedata traindir/ posteriordir/ segway annotate signal.genomedata traindir/ identifydir/ # Output: identifydir/segway.bed.gz (state assignments)
Segway uses continuous signal (from the genomedata archive) vs ChromHMM's binarized bins. Trade-off: more information per region (continuous) but more complex training.
EpiLogos Visualization
EpiLogos doesn't perform segmentation; it visualizes existing ChromHMM/Segway segmentations across many biosamples (epilogos.org).
bash# Use precomputed ChromHMM segmentations across multiple cell types # Web interface: https://epilogos.altius.org/ # Local: github.com/meuleman/epilogos
Useful for: cross-cell-type comparison; identifying tissue-specific regulatory states; cohort-level chromatin landscape summaries.
Full-Stack ChromHMM (Vu Ernst 2022)
The full-stack model trained on 1032 datasets / 127 reference epigenomes:
bash# Use precomputed model from Ernst lab # github.com/ernstlab/full_stack_ChromHMM_annotations # Annotate new sample by applying model to binarized data java -mx16G -jar ChromHMM.jar MakeSegmentation \ full_stack_model_100states.txt \ binarized_sample/ \ full_stack_output/
Useful for: applying a comprehensive cross-tissue annotation to a new sample; comparing to canonical Roadmap states.
Per-Tool Failure Modes
ChromHMM -- Bin size 200 bp too coarse for sharp boundaries
Trigger: Studying TF binding boundaries or sharp enhancer transitions at 200 bp resolution.
Mechanism: ChromHMM default 200 bp bins; biology may shift within a bin.
Fix: Reduce to -b 100 or -b 50 (smaller bin); increases memory and compute time but improves boundary resolution. Re-train model at finer resolution.
ChromHMM -- Binarization throws away signal quantitation
Trigger: Distinguishing low- from high-signal regions of the same state.
Mechanism: ChromHMM binarizes each 200 bp bin to 0/1 per mark; state assignment uses combinatorial pattern, not magnitude.
Fix: Use Segway (continuous signal) or EpiSegMix (flexible distributions) for magnitude-aware segmentation.
ChromHMM / Segway -- Wrong state count
Trigger: Training with N=50 states on a 4-mark panel; or N=10 on a 7-mark panel.
Mechanism: Excess states fragment biology; insufficient states force unrelated regions into the same state.
Symptom: Emission matrix shows redundant states (multiple states with same emission profile) at high N; or biologically distinct regions lumped together at low N.
Fix: Train at N=15, 18, 25; inspect emission matrix similarity; choose the smallest N where states are interpretable as distinct biology.
Mark panel mismatch with model
Trigger: Applying Roadmap 25-state model to a sample with different mark panel.
Mechanism: Model was trained on specific marks; emission probabilities are mark-specific. Applying to different mark panel produces nonsensical state assignments.
Fix: Either train a new model on the available mark panel; OR ensure the exact same marks (and ordering) as used in the model.
IgG control instead of input
Trigger: Using IgG controls in BinarizeBam for histone marks.
Mechanism: ChromHMM's binarization compares mark signal to control; histone mark biology assumes input (sonicated chromatin) as background, not IgG.
Fix: Use sonicated input as control for histone mark ChIP. IgG is not appropriate for ChromHMM binarization of histone marks.
Cross-cell-type state mapping
Trigger: Training separate models per cell type and trying to compare state assignments.
Mechanism: State 5 in cell type A may not correspond to state 5 in cell type B if trained independently.
Fix: Train one model on concatenated data from all cell types (joint segmentation); or apply a single precomputed model (Roadmap 15-state, full-stack) to all samples for consistent state labels.
Reconciliation
| Pattern | Likely cause | Action |
|---|---|---|
| ChromHMM and Segway segments differ | Different bin sizes / binarization vs continuous | Both can be valid; inspect emission matrices; pick the tool matching the resolution needs |
| State assignment varies wildly between replicates | Insufficient marks; over-binned | Increase mark panel; reduce state count |
| Active TSS state overlaps polycomb state at promoters | Bivalent biology (Bernstein 2006) | Expected for ESC-like cells; not an error; consider bivalent-specific state in N=18 model |
| Roadmap 25-state model annotates unknown cell type | Cross-cell-type generalization | Use cautiously; verify against tissue-specific tracks |
| Heterochromatin (H3K9me3) state has too many bins | H3K9me3 covers large fraction of genome | Expected; heterochromatin is genome-wide |
Common Errors
| Error / symptom | Cause | Solution |
|---|---|---|
java.lang.OutOfMemoryError | Insufficient JVM heap | java -mx32G -jar ChromHMM.jar ... |
BinarizeBam very slow | Large BAMs without index | samtools index all BAMs first |
| All states have similar emissions | Mark panel too small | Need at least 5 marks for canonical 15-state model |
| Segments file empty for some chromosomes | Chromosome not in chromsizes file | Add or use -chrom flag to restrict |
| State labels don't match Roadmap | Trained model independently | Use Roadmap precomputed model OR map states by emission similarity |
| ChromHMM "no signal in marks" | All bins binarized to 0 | Check signal quality; verify control normalization |
References
- Ernst J & Kellis M 2012 Nat Methods 9:215 (ChromHMM v1)
- Ernst J & Kellis M 2017 Nat Protoc 12:2478 (ChromHMM protocol)
- Hoffman MM et al 2012 Nat Methods 9:473 (Segway)
- Schmitz JE, Aggarwal N, Laufer L, Walter J, Salhab A, Rahmann S 2024 Bioinformatics 40:btae178 (EpiSegMix)
- Meuleman W et al 2020 Nature 584:244 (EpiLogos / DHS index)
- Zhang Y & Hardison 2016 Nucleic Acids Res 44:6721 (IDEAS)
- Mammana A & Chung HR 2015 Genome Biol 16:151 (EpiCSeg)
- Roadmap Epigenomics Consortium 2015 Nature 518:317 (Roadmap 25-state model)
- Vu H & Ernst J 2022 Genome Biol 23:9 (full-stack ChromHMM)
- Kundaje A et al 2015 Nature 518:317 (Roadmap integrative analysis)
Related Skills
- chip-seq/peak-calling - Peak calling per mark before segmentation
- chip-seq/chipseq-qc - Replicate concordance per mark; QC before integration
- chip-seq/peak-annotation - cCRE classification (PLS/pELS/dELS) complementary to chromatin states
- chip-seq/spike-in-normalization - Spike-in normalize per-mark BAMs before binarization for cross-condition state comparison
- atac-seq/single-cell-atac - scATAC + ChIP integration via multimodal methods
- machine-learning/model-validation - Model selection (state count); cross-validation
- data-visualization/genome-tracks - Visualize state segmentations
- gene-regulatory-networks/coexpression-networks - Cross-reference chromatin states with co-expression modules
